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Developing Solutions that Improve Technologies
Published in Chiang H. Ren, The Fundamentals of Developing Operational Solutions for the Government, 2018
Autonomic Computing: Systems that operate across their range of functions and complete missions without human involvement. Self-driving cars are a prime example of emerging autonomic computing technology. In theory, a broad range of devices can have embedded computing that automates operations and reduces the need for human control and decision-making. These pervasive devices can further coordinate activities across networks, and the range of their capabilities is only constrained by each device’s ability to perform high-order situational analysis functions. As no computer can yet match the human mind’s ability to handle unbounded situations, autonomic computing tends to be more successful for functions with defined ranges such as established road systems and limited behaviors for surrounding vehicles.
Technology, virtuality and utopia
Published in Mireille Hildebrandt, Antoinette Rouvroy, Law, Human Agency and Autonomic Computing, 2011
The subject itself is highly uncertain. ‘Autonomic computing’ per se, is difficult to circumscribe as an object for legal theoretical inquiry. IBM, which first coined the term, explicitly acknowledges that ‘the definition of autonomic computing will likely transform as contributing technologies mature’. IBM nevertheless lists eight defining characteristics for it, and presents the vision of ‘computer systems that regulate themselves much in the same way our autonomic nervous system regulates and protects our bodies’. The defining capabilities of autonomic computing include self-knowledge (the system must somehow know itself and be able to identify its own components), autonomic and dynamic self-reconfiguration and adjustment, constant optimisation of its own working, self-prevention and reparation of malfunctioning caused by internal or external events, detection of and protection from attacks against the system's security and integrity, context awareness and autonomic adaptation of itself or even the environment to the circumstances, ability to anticipate and optimise resources consumption while keeping its complexity hidden. It must marshal I/T resources to shrink the gap between the business or personal goals of the user, and the I/T implementation necessary to achieve those goals – without involving the user in that implementation.1
Machine Learning (ML) Algorithms for Enabling the Cognitive Internet of Things (CIoT)
Published in Pethuru Raj, Anupama C. Raman, Harihara Subramanian, Cognitive Internet of Things, 2022
Pethuru Raj, Anupama C. Raman, Harihara Subramanian
The world is yet to see positive disruptions through the unique marriage of the Internet of Things (IoT) with the AI paradigm, which led to coin a new term, “cognitive IoT (CIoT)”. We will have CIoT systems, services, and solutions in plenty in the years to come. Our everyday environments with CIoT devices are getting traction, and they will exhibit innovative behaviour that helps to enhance humankind. The owners and occupants of any CIoT environment can receive context-aware, people-centric, event-driven, and knowledge-filled services in a discreet manner. This strategically sound synchronization results in intelligent and sophisticated processes that simplify and speed up the goals of ambient intelligence. This unique combination can also inject human cognition into our everyday devices, types of machinery, equipment, appliances, and instruments. Resultantly, we will have pervasive computing, adaptive communication, and ubiquitous sensing, perception, vision, and action. Intelligence becomes ambient, and commonly found materials in our midst become sentient to join in the mainstream computing. And all kinds of electronic systems can automatically learn out of data supplied and received, adroitly answer for queries, creatively propose new theories, and substantiate the ideas with appropriate shreds of evidence, and naturally interact with humans. IBM publications quotes, autonomic computing is all about empowering machines to be self-configuring, self-diagnosing and healing, self-governing, optimizing and defending, and self-managing. Autonomy is all set and gets driven through cognition.
A resilient hierarchical distributed model of a cyber physical system
Published in Cyber-Physical Systems, 2023
A knowledgebase of an RCPS unit contains various types of information and data for the RCPS unit to operate, monitor, control, protect, and adapt to changes in the system as well as the environment. An RCPS can be viewed as a self-adaptive system. Several research work in self-adaptive systems and robotics have proposed and used a knowledgebase for adaptation. The nature of the knowledgebase of a self-adaptive system depends upon the complexity-level of the self-adaptive system. An IBM whitepaper [59] provides a comprehensive description of an autonomic computing system that has a built-in self-adaptiveness. The autonomic computing system has five major building blocks – Manageability endpoints, Knowledge sources, Autonomic managers, and Manual managers [59] that bring in the ability to self-adapt. An autonomic manager has four functions – Monitor, Analyse, Plan, and Execute (MAPE). These functions use various kinds of knowledge resources. The monitor, analyse, plan, execute, knowledge (MAPE-K) feedback loop has been used as a basic concept in self-managed systems. Based upon the MAPE-K Feedback Loop [59], an adaptive system should have four types of knowledge – knowledge about itself (Ksys), its environment (Kenv), its goal (Kgoal), and the adaption (Kadapt) [30]. We suggest that each RCPS unit should maintain an appropriate level of Ksys, Kenv, Kgoal, and Kadapt. We expound these types of knowledge in terms of their content and possible structure.
Ontology-Assisted and Autonomic Testing Verified Model for Automated and Reliable Web Development
Published in IETE Journal of Research, 2022
Kapil Juneja, Vipul Kumar Mishra, Dhiraj Khurana
Security features, modularity, scalability, and robustness are the common gaps between software engineering and web development. Another key issue or support not handled by the young developers is the web system accessibility for visually impaired users [31]. Kumar et al. [32] have designed an autonomic computing-based framework to identify the security threats in the software system. The system can provide self-healing against these software vulnerabilities. The intelligent framework optimized the security aspects and improved the security risks that exist in software development. The usability aspect of the framework is missing as the framework is not applied in real time. The subsequent stage of testing is not accomplished to verify the significance of the proposed system. Whereas, for handling security threats, the server-side authentication code is also included for form controls. The session management and the authentication restrictions are also validated automatically during the design of the website [33,34]. The major loops in security are taken care of by this proposed web engineering tool.
An overview of self-engineering systems
Published in Journal of Engineering Design, 2021
In 2001, IBM highlighted the problem that increasingly complex software systems were becoming impossible for a human to manage and introduced the idea of autonomic computing (Kephart and Chess 2003). Inspired by the human body’s autonomic nervous system, the key aim was to enable the software to meet a higher aim or set policy despite possible changes. A key function of these systems was the ability to self-manage which required a system to have self-configuration, self-optimising, self-healing and self-protecting abilities.